A data mining algorithm for fuzzy transaction data

• Published in: Quality & quantity Vol. 48; no. 6; pp. 2963 - 2971
• Main Authors: Chen, Chin-Yuan, Liang, Gin-Shuh, Su, Yuhling, Liao, Mao-Sheng
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.11.2014; Springer; Springer Nature B.V
• Subjects: Algorithms; Ambiguity; Consumption; Data; Data mining; Decision makers; Decision making; Evaluation; Expenditures; Fuzzy sets; Judgement; Knowledge; Knowledge management; Linguistics; Methodology of the Social Sciences; Set theory; Social Sciences; Statistical analysis; Fuzzy similarity; Trapezoidal fuzzy numbers; Data mining; Association rule
• ISSN: 0033-5177; 1573-7845
• DOI: 10.1007/s11135-013-9934-1

The main purpose of this paper is to propose a data mining algorithm for finding interesting association rules from given sets of fuzzy transaction data. To efficiently resolve the ambiguity frequently arising in available information and do more justice to the essential fuzziness in human judgment and preference, the trapezoidal fuzzy numbers are used to describe the fuzzy assessments of transaction data. Then, combining the concepts of fuzzy set theory and the priori algorithms, the interesting item sets are found to construct the association rules. Finally, a numerical example is used to demonstrate the computational process of proposed data mining algorithm. By utilizing this data mining algorithm, the decision-makers’ fuzzy assessments with various rating attitudes can be taken into account in the data mining process to assure more convincing and accurate knowledge discovery.